ESS 330 - Daily Exercise 21

Library Code

library(tidyverse)

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library(tidymodels)

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library(tsibble)

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  as_tibble.grouped_df dplyr

Attaching package: 'tsibble'

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library(feasts)

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library(dataRetrieval)
library(plotly)

Attaching package: 'plotly'

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Data Import

# Example: Cache la Poudre River at Mouth (USGS site 06752260)
poudre_flow <- readNWISdv(siteNumber = "06752260",    # Download data from USGS for site 06752260
                          parameterCd = "00060",      # Parameter code 00060 = discharge in cfs)
                          startDate = "2013-01-01",   # Set the start date
                          endDate = "2023-12-31") |>  # Set the end date
  renameNWISColumns() |>                              # Rename columns to standard names (e.g., "Flow", "Date")
  mutate(Date = yearmonth(Date)) |>                   # Convert daily Date values into a year-month format (e.g., "2023 Jan")
  group_by(Date) |>                                   # Group the data by the new monthly Date
  summarise(Flow = mean(Flow))                       # Calculate the average daily flow for each month

GET:https://waterservices.usgs.gov/nwis/dv/?site=06752260&format=waterml%2C1.1&ParameterCd=00060&StatCd=00003&startDT=2013-01-01&endDT=2023-12-31

Converting Data Frame and Extracting Components

# Converting to Tibble Frame
tibble_poudre <- as_tibble(poudre_flow) |> 
  as_tsibble(index = Date)

# Creating Model
poudre_decomp <- tibble_poudre |>
  model(STL(Flow ~ season(window = "periodic")))

# Extracting Components
poudre_components <- components(poudre_decomp)

Plotting the Time Series Analysis

# Plotting Data Series 
poudre_plot <- poudre_components |> 
  autoplot() +
  labs(title = "STL Decomposition of Poudre Flow",
       y = "Flow (cfs)", x = "Date") +
  theme_minimal()

poudre_plot

# Animating the Plot
poudre_plotly <- ggplotly(poudre_plot)

poudre_plotly

Visualizing Seasonal Patterns

poudre_subseries <- tibble_poudre |> 
  gg_subseries(Flow) +
  labs(title = "Seasonal Subseries Plot of Poudre River Flow",
       y = "Average Flow (cfs)", x = "Month") +
  theme_minimal()

poudre_subseries

Data Analysis

With each of the time series plots, we can clearly see that there is a spike in the flow of the gauge around May and June. This makes sense as this is when the heaviest amount of rain is typically found in Northern Colorado. There is also the fact that over the years, there is an overall decrease in the amount of flow, meaning that there is less precipitation to help initiate flow. The sub-series allows for the data to be sorted by the months rather than a linear, and shows if there is any outliers that may screw the analysis of the seasons. We can see this specifically in September and slightly in April, but with all of the data sorted by month, we can see the average flow for each month and easily find the season with the highest amount of flow.